Network approach reveals the spatiotemporal influence of traffic on air pollution under COVID-19

空气污染 空气质量指数 北京 污染 环境科学 中国 气象学 环境工程 地理 生态学 生物 考古 有机化学 化学
作者
Weiping Wang,Saini Yang,Kai Yin,Zhi-Dan Zhao,Na Ying,Shlomo Havlin
出处
期刊:Chaos [American Institute of Physics]
卷期号:32 (4) 被引量:7
标识
DOI:10.1063/5.0087844
摘要

Air pollution causes widespread environmental and health problems and severely hinders the quality of life of urban residents. Traffic is critical for human life, but its emissions are a major source of pollution, aggravating urban air pollution. However, the complex interaction between traffic emissions and air pollution in cities and regions has not yet been revealed. In particular, the spread of COVID-19 has led various cities and regions to implement different traffic restriction policies according to the local epidemic situation, which provides the possibility to explore the relationship between urban traffic and air pollution. Here, we explore the influence of traffic on air pollution by reconstructing a multi-layer complex network base on the traffic index and air quality index. We uncover that air quality in the Beijing–Tianjin–Hebei (BTH), Chengdu–Chongqing Economic Circle (CCS), and Central China (CC) regions is significantly influenced by the surrounding traffic conditions after the outbreak. Under different stages of the fight against the epidemic, the influence of traffic in some regions on air pollution reaches the maximum in stage 2 (also called Initial Progress in Containing the Virus). For the BTH and CC regions, the impact of traffic on air quality becomes bigger in the first two stages and then decreases, while for CC, a significant impact occurs in phase 3 among the other regions. For other regions in the country, however, the changes are not evident. Our presented network-based framework provides a new perspective in the field of transportation and environment and may be helpful in guiding the government to formulate air pollution mitigation and traffic restriction policies.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
末位牛马完成签到,获得积分10
1秒前
kki完成签到,获得积分10
1秒前
zl完成签到 ,获得积分10
2秒前
2秒前
充电宝应助寒冷的元芹采纳,获得10
2秒前
KEYANKANG完成签到,获得积分10
3秒前
liu完成签到,获得积分10
3秒前
希望天下0贩的0应助lcxszsd采纳,获得10
4秒前
5秒前
lip完成签到,获得积分10
5秒前
Tsuki完成签到 ,获得积分10
6秒前
cody发布了新的文献求助10
7秒前
yuanbenshimao完成签到 ,获得积分10
8秒前
求助人员发布了新的文献求助10
9秒前
9秒前
ashdj发布了新的文献求助30
9秒前
头发很多发布了新的文献求助10
10秒前
zzzz完成签到,获得积分10
11秒前
GYYly完成签到,获得积分10
11秒前
11秒前
脑洞疼应助lip采纳,获得10
14秒前
JamesPei应助ACEmeng采纳,获得10
14秒前
Lucas应助Remaking采纳,获得10
14秒前
Lucas应助米尼采纳,获得10
15秒前
dreamode发布了新的文献求助30
16秒前
小蚊子应助科研小白菜采纳,获得30
16秒前
racill发布了新的文献求助10
16秒前
外向的初曼完成签到,获得积分10
17秒前
18秒前
顺心囧完成签到 ,获得积分10
18秒前
吃狼的羊发布了新的文献求助10
20秒前
20秒前
你好鸭完成签到 ,获得积分10
22秒前
江江关注了科研通微信公众号
22秒前
24秒前
新司机完成签到,获得积分10
24秒前
芝芝莓莓完成签到 ,获得积分10
25秒前
沐一发布了新的文献求助30
25秒前
赘婿应助包包包包采纳,获得10
25秒前
小胜完成签到 ,获得积分10
26秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Kinesiophobia : a new view of chronic pain behavior 2000
Research for Social Workers 1000
Psychology and Work Today 800
Mastering New Drug Applications: A Step-by-Step Guide (Mastering the FDA Approval Process Book 1) 800
Kinesiophobia : a new view of chronic pain behavior 600
Signals, Systems, and Signal Processing 510
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5895806
求助须知:如何正确求助?哪些是违规求助? 6706758
关于积分的说明 15732310
捐赠科研通 5018331
什么是DOI,文献DOI怎么找? 2702500
邀请新用户注册赠送积分活动 1649180
关于科研通互助平台的介绍 1598460